| API & CONOPS Document | Participation Agreement | FAQ | irex@nist.gov |
The IREX 10: Identification Track assesses iris recognition performance for identification (a.k.a one-to-many) applications. Most flagship deployments of iris recognition operate in identification mode, providing services ranging from prison management, border security, expedited processing, and distribution of resources. Administered at the Image Group’s Biometrics Research Lab (BRL), developers submit their iris recognition software for testing over datasets sequestered at NIST. As an ongoing evaluation, developers may submit at any time.
Two-eye Accuracy:
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence) |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
The number after the ± indicates either the 90% confidence interval (for accuracy) or the standard deviation (for times and sizes).
Single-eye Accuracy:
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence) |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | One eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
Core accuracy for the identification task can be characterized by Detection-error trade-off (DET) plots. Generally, curves lower down in a DET plot correspond to more accurate matchers. The plots are interactive through the use of the Plotly.js graphing library.
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |